{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nearest Neighbors\n",
    "### Agenda\n",
    "1. Fundamentals of Nearest Neighbor\n",
    "2. Unspervised Nearest Neighbors\n",
    "3. Nearest Neighbors for Classification\n",
    "4. Nearest Neighbors for Regression\n",
    "5. Nearest Centroid Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Fundamentals of Nearest Neighbor\n",
    "* These are non-generalizing learning models .i.e simply stores all the training data.\n",
    "* Stores data into fast access data structure like Ball Tree & KD Tree\n",
    "* The principle behind nearest neighbor methods is to find a predefined number of training samples closest in distance to the new point, and predict the label/value from these.\n",
    "* The number of samples can be a user-defined constant or configured radius\n",
    "* Very useful when decision boundry is very irregular."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Unsupervised Nearest Neighbor\n",
    "* Based on the algorithm configured training data is stored\n",
    "* For an unknown data, return the shortest Euclidean distance between configured k \n",
    "* Euclidean distance is calculated as the square root of the sum of the squared differences between a new point (x) and an existing point (xi) across all input attributes j."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import NearestNeighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "nn = NearestNeighbors(n_neighbors=2, algorithm='ball_tree')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_blobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,_ = make_blobs(n_features=2, n_samples=10, cluster_std=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1d23b952278>"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fH3URR2lNryuVZAp4PnDbaCtZMR9grvN0YNSFtLDmnni11No/zF3v05j7Z+hZwPVJTq1VPlVpmWt6O/DS4Va0Mg5nnaYkVzE3hHDtMGtbQYe9rtS4SbIB+BRwRVX9aNT1DCrJhcCDVbUtyW+Pup4W1lzgL7X2T5JLgRvmA/7rSQ4wt27G7LDqOxqLXVOS5wLPBO5MAnPDHrcnObuqHhhiiUdluXWaklwCXAict9o/lJdw2OtKjZMkxzEX9tdW1Q2jrmeFnANclOTlwC8AT0nyiap67YjrWjFdzcNP8qfASVX1jiSnA18CJsc4TH5Okl3AdFWN28JPT5DkAuB9wIuralV/IC8lybHM3XQ+D/g+8A3gD+eXHxlLmetd/D3w31V1xajraWG+h/+Wqrpw1LWspN7G8K8BTk1yN3M3zy5ZK2G/Bn0YeDJwc5I7knx01AUdjfkbz28EbmLu5ub14xz2884BXgecO//e3DHfK9Yq11UPX5J61lsPX5K6ZeBLUicMfEnqhIEvSZ0w8CWpEwa+JHXCwJekThj4ktSJ/wdmn4zpgJODdwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d23d6fed68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0], X[:,1], s=10,alpha=.5)\n",
    "plt.scatter([5],[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(algorithm='ball_tree', leaf_size=30, metric='minkowski',\n",
       "         metric_params=None, n_jobs=1, n_neighbors=2, p=2, radius=1.0)"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[1.15317476, 1.89964951]]), array([[6, 9]], dtype=int64))"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn.kneighbors([[5,5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "_,X_nearest = nn.kneighbors([[5,5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "nearest = X[X_nearest[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 9]], dtype=int64)"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_nearest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1d23d8393c8>"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d23b952400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0], X[:,1], s=10,alpha=.5)\n",
    "plt.scatter(nearest[:,0], nearest[:,1], s=10 ,cmap='virdis')\n",
    "plt.scatter([5],[5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Nearest Neighbors for Classification\n",
    "* A query point is assigned the data class which has the most representatives within the nearest neighbors of the point.\n",
    "* Two types of nearest neighbor classifier\n",
    "  - KNeighboursClassifier ( based on configured k )\n",
    "  - RadiusNeighbourClassifier ( based on configured r )\n",
    "* Weights can be ‘uniform’ or ‘distance’. It  assigns weights proportional to the inverse of the distance from the query point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier,RadiusNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "knc = KNeighborsClassifier(n_neighbors=5)\n",
    "rnc = RadiusNeighborsClassifier(radius=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,Y = make_blobs(n_features=2, n_samples=50, cluster_std=5, centers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1d2356b1d68>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d235aad908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0], X[:,1], s=50,alpha=.5, c=Y, cmap='winter')\n",
    "plt.scatter([5],[-5],c='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RadiusNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "             metric_params=None, outlier_label=None, p=2, radius=5,\n",
       "             weights='uniform')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knc.fit(X,Y)\n",
    "rnc.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "knc_pred = knc.predict([[5,-5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knc_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0.72517301, 1.94212795, 2.44043399, 2.52049112, 3.24551058]]),\n",
       " array([[17, 37, 32, 15,  1]], dtype=int64))"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knc.kneighbors([[5,-5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "_, knc_neighbors = knc.kneighbors([[5,-5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[17, 37, 32, 15,  1]], dtype=int64)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knc_neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnc_pred = rnc.predict([[5,-5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnc_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([array([3.24551058, 4.03329819, 3.33286427, 3.49092452, 2.52049112,\n",
       "        0.72517301, 4.80582379, 2.44043399, 4.35485471, 3.61289927,\n",
       "        1.94212795, 3.45846403, 4.73004628])], dtype=object),\n",
       " array([array([ 1,  5,  8, 10, 15, 17, 29, 32, 33, 36, 37, 38, 42], dtype=int64)],\n",
       "       dtype=object))"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnc.radius_neighbors([[5,-5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "_, rnc_neighbors = rnc.radius_neighbors([[5,-5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  5,  8, 10, 15, 17, 29, 32, 33, 36, 37, 38, 42], dtype=int64)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnc_neighbors[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing for NearestNeighbourClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1d23733ea58>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d2372b8f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nearest = X[knc_neighbors[0]]\n",
    "plt.scatter(X[:,0], X[:,1], s=50,alpha=.5, c=Y, cmap='winter')\n",
    "plt.scatter([5],[-5],c='r')\n",
    "plt.scatter(nearest[:,0], nearest[:,1], s=10, c='k')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing for RadiusNeighbourClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1d2372dbd30>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d2371d5668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nearest = X[rnc_neighbors[0]]\n",
    "plt.scatter(X[:,0], X[:,1], s=50,alpha=.5, c=Y, cmap='winter')\n",
    "plt.scatter([5],[-5],c='r')\n",
    "plt.scatter(nearest[:,0], nearest[:,1], s=10, c='k')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Understanding weights - Nearer neighbor has more impact when configured with weight='distance' parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnc = RadiusNeighborsClassifier(radius=5, weights='distance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Impact of number of neighbors on decision boundry\n",
    "<img src=\"https://github.com/awantik/machine-learning-slides/blob/master/mlnn4.png?raw=true\">\n",
    "\n",
    "<img src=\"https://github.com/awantik/machine-learning-slides/blob/master/mlnn3.png?raw=true\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Nearest Neighbors for Regression\n",
    "* The labels of data are continues\n",
    "* The label assigned to a query point is computed based the mean/medium of the labels of its nearest neighbors.\n",
    "* Neighbors to be considered can be based on count or radius distance\n",
    "  - KNeighbourRegressor\n",
    "  - RadiusNeighbourRegressor\n",
    "* 'weight' parameter to control impact of neighbor based on closeness\n",
    "* NearestNeighborRegressor is capable of predicting mutiple outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_olivetti_faces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "faces = fetch_olivetti_faces()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "targets = faces.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d237235518>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d2372aa7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(faces.images[11], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 64, 64)"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faces.images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 4096)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faces.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = faces.data[targets < 30]\n",
    "test = faces.data[targets >= 30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d237021a20>"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d2370f0208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(train[0][:2048].reshape(32,64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX = train[:,:2048]\n",
    "trainY = train[:,2048:]\n",
    "testX = test[:,:2048]\n",
    "testY = test[:,2048:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "nn_r = KNeighborsRegressor(n_neighbors=20,weights='distance', n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "          metric_params=None, n_jobs=-1, n_neighbors=20, p=2,\n",
       "          weights='distance')"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn_r.fit(trainX,trainY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = testX[::10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = nn_r.predict(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "final = np.hstack([sample,res])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d2370a2860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d2371f2470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d2375b5e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d235c041d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d236fca470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d2374bfac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d237543940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d237ee8fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d237588b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d2373f47b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for img in final:\n",
    "    plt.imshow(img.reshape(64,64), cmap='gray')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nearest Centroid Classifier\n",
    "* Used for classification\n",
    "* Computes the centroid for each class.\n",
    "* Measures the distance ( generally Euclidean ) of the data point X to the centroid of each class.\n",
    "* If the distance is of X and the centroid of a particular class is minimum then it assigns that class to the data point X ( The argmin statement in the picture you have provided ) i.e Y predicts the centroid closest to the point X\n",
    "* It's different from KMeans, since KMeans identifies the clusters & here we assign data to cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors.nearest_centroid import NearestCentroid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "nc = NearestCentroid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestCentroid(metric='euclidean', shrink_threshold=None)"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nc.fit(iris.data, iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9266666666666666"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nc.score(iris.data, iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
